--- title: DualGAN training loop keywords: fastai sidebar: home_sidebar summary: "Defines the loss and training loop functions/classes for DualGAN." description: "Defines the loss and training loop functions/classes for DualGAN." nb_path: "nbs/08_train.dualgan.ipynb" ---
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set_seed(999, reproducible=True)
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class DualGANLoss[source]

DualGANLoss(dualgan:Module, l_adv:float=1.0, l_rec:float=1.0, l_idt:float=0.0) :: Module

DualGAN loss function. The individual loss terms are also atrributes of this class that are accessed by fastai for recording during training.

Attributes:

self.dualgan (nn.Module): The DualGAN model.

self.l_A (float): lambda_A, weight of domain A losses.

self.l_B (float): lambda_B, weight of domain B losses.

self.crit (AdaptiveLoss): The adversarial loss function (either a BCE or MSE loss depending on lsgan argument)

self.real_A and self.real_B (fastai.torch_core.TensorImage): Real images from domain A and B.

self.gen_loss (torch.FloatTensor): The generator loss calculated in the forward function

self.cyc_loss (torch.FloatTensor): The cyclic loss calculated in the forward function

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compute_gradient_penalty[source]

compute_gradient_penalty(D, real_samples, fake_samples)

Calculates the gradient penalty loss for WGAN GP

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class DualGANTrainer[source]

DualGANTrainer(n_crit=2, clip_value=0.1, l_gp=None) :: Callback

Learner Callback for training a DualGAN model.

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dual_learner[source]

dual_learner(dls:DataLoader, m:DualGAN, opt_func=RMSProp, loss_func=DualGANLoss, show_imgs:bool=True, imgA:bool=True, imgB:bool=True, show_img_interval:bool=10, metrics:list=[], cbs:list=[], lr=0.001, splitter=trainable_params, path=None, model_dir='models', wd=None, wd_bn_bias=False, train_bn=True, moms=(0.95, 0.85, 0.95))

Initialize and return a Learner object with the data in dls, DualGAN model m, optimizer function opt_func, metrics metrics, and callbacks cbs. Additionally, if show_imgs is True, it will show intermediate predictions during training. It will show domain B-to-A predictions if imgA is True and/or domain A-to-B predictions if imgB is True. Additionally, it will show images every show_img_interval epochs. OtherLearner` arguments can be passed as well.

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Quick Test

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horse2zebra = untar_data('https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/horse2zebra.zip')
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folders = horse2zebra.ls().sorted()
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trainA_path = folders[2]
trainB_path = folders[3]
testA_path = folders[0]
testB_path = folders[1]
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dls = get_dls(trainA_path, trainB_path,num_A=100)
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dual_gan = DualGAN()
learn = dual_learner(dls, dual_gan,show_img_interval=1)
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learn.show_training_loop()

Start Fit
   - before_fit     : [TrainEvalCallback, ShowImgsCallback, Recorder, ProgressCallback]
  Start Epoch Loop
     - before_epoch   : [Recorder, ProgressCallback]
    Start Train
       - before_train   : [TrainEvalCallback, DualGANTrainer, Recorder, ProgressCallback]
      Start Batch Loop
         - before_batch   : [DualGANTrainer]
         - after_pred     : [DualGANTrainer]
         - after_loss     : []
         - before_backward: []
         - before_step    : []
         - after_step     : []
         - after_cancel_batch: []
         - after_batch    : [TrainEvalCallback, Recorder, ProgressCallback]
      End Batch Loop
    End Train
     - after_cancel_train: [Recorder]
     - after_train    : [Recorder, ProgressCallback]
    Start Valid
       - before_validate: [TrainEvalCallback, Recorder, ProgressCallback]
      Start Batch Loop
         - **CBs same as train batch**: []
      End Batch Loop
    End Valid
     - after_cancel_validate: [Recorder]
     - after_validate : [Recorder, ProgressCallback]
  End Epoch Loop
   - after_cancel_epoch: []
   - after_epoch    : [ShowImgsCallback, Recorder]
End Fit
 - after_cancel_fit: []
 - after_fit      : [ProgressCallback]
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test_eq(type(learn),Learner)
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learn.fit_flat_lin(5,5,2e-4)
epoch train_loss adv_loss_A adv_loss_B rec_loss_A rec_loss_B D_A_loss D_B_loss time
0 -1.225994 -0.965739 -0.931990 0.354460 0.353114 -0.010898 -0.002097 00:06
1 -1.383732 -0.999190 -0.998738 0.258967 0.261373 -0.000011 -0.000268 00:06
2 -1.448422 -0.999503 -0.999257 0.244793 0.240065 0.000066 -0.000068 00:06
3 -1.488793 -0.999653 -0.999547 0.240394 0.223500 0.000069 -0.000088 00:07
4 -1.529549 -0.999728 -0.999568 0.212509 0.204648 -0.000021 -0.000083 00:07
5 -1.574171 -0.999754 -0.999664 0.190567 0.178492 0.000010 -0.000069 00:08
6 -1.633542 -0.999816 -0.999724 0.143122 0.139768 0.000010 -0.000058 00:08
7 -1.675023 -0.999844 -0.999737 0.129500 0.133948 0.000027 -0.000105 00:08
8 -1.706186 -0.999842 -0.999745 0.118132 0.129545 0.000022 -0.000052 00:09
9 -1.726011 -0.999837 -0.999760 0.118993 0.126145 -0.000012 -0.000052 00:09 
/home/tmabraham/anaconda3/lib/python3.7/site-packages/fastprogress/fastprogress.py:74: UserWarning: Your generator is empty.
  warn("Your generator is empty.")
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learn.recorder.plot_loss(with_valid=False)
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